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STATISTICAL ANALYSIS OF MICROARRAY DATA AND FUNCTIONAL
GENOMICS OF YEAST AGEING
by
Huanying Ge
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTATIONAL BIOLOGY AND BIOINFORMATICS)
August 2009
Copyright 2009 Huanying Ge

This dissertation focuses on the computational biology researches using Affymetrix microarray technology. It consists of two parts, focusing on two different levels of statistical analysis of microarray data.; In the first part, I describe the proposed Probe-Treatment-Reference (PTR) model, which can streamline normalization and summarization. Normalization aims to remove non-biological variations across different arrays. The normalization algorithms generally require the specification of reference and target arrays. However, the issue of reference selection has not been fully addressed. Summarization aims to estimate the transcript abundance from normalized intensities. In the PTR method, we considered normalization and summarization jointly by a new strategy of reference selection. It is a general framework to deal with the issue of reference selection and can readily be applied to existing normalization algorithms such as the invariant-set, sub-array and quantile method. In the PTR summarization, we estimate parameters in the model by the Least Absolute Deviations (LAD) approach and implement the computation by median polishing. I will show that the LAD estimator is robust in the sense that it has bounded influence in the three-factor PTR model. This model fitting, implicitly, defines an "optimal reference" for each probe-set. I evaluated the effectiveness of the PTR method by two Affymetrix spike-in data sets. Our method reduces the variations of non-differentially expressed genes and thereby increases the detection power of differentially expressed genes. This microarray pre-processing method provided a solid basis for our later investigation of gene expression.; In the second part, I describe our comparative analyses of gene expression profiles of the long-lived yeast sch9Δ mutant and the wild type. The yeast sch9Δ mutant has a smaller cell size and lives three times as long as the wild type chronologically. In order to investigate the changes of biological activities and pathways that account for the longevity extension, we measured their gene expression profiles every 12 hours from 12 to 120 hours in the synthetic dextrose complete medium. We processed the microarray data based on the PTR model, in which pairwise sub-array normalization was first implemented and then the normalized arrays were summarized into expression values for each probe-set and each sample. We compared the expression profiles and time difference profiles between the two strains and interpret them by statistical inferences based on biological knowledge including Gene Ontology, KEGG pathway, and protein localization data. The results implied that the sch9Δ mutant followed a different metabolic path during the chronological ageing as indicated by two observations: (i) mitochondrial ribosome genes were not up-regulated in the sch9Δ mutant as in the wild type after diauxic-shift; (ii) electron transport, oxidative phosphorylation and TCA were down-regulated earlier. In addition, between 12 and 24 hours, we observed the following in the sch9Δ mutant: (i) stress response genes were up-regulated by larger fold changes; (ii) rRNA processing genes were down-regulated more dramatically. Moreover, these rRNA processing genes were more volatile in the sch9Δ mutant, and three associated cis-regulatory elements and one factor, Azf1, were identified. We also observed that the up-regulation of TCA and electron transport was accompanied by deep down-regulation of rRNA processing over time in sch9Δ.; Together, these findings provided new insights into the longevity programme turned on by the sch9Δ mutant.

STATISTICAL ANALYSIS OF MICROARRAY DATA AND FUNCTIONAL
GENOMICS OF YEAST AGEING
by
Huanying Ge
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTATIONAL BIOLOGY AND BIOINFORMATICS)
August 2009
Copyright 2009 Huanying Ge